{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _*H2 energy plot comparing full to particle hole transformations*_\n",
    "\n",
    "This notebook demonstrates using Qiskit Chemistry to plot graphs of the ground state energy of the Hydrogen (H2) molecule over a range of inter-atomic distances using VQE and UCCSD with full and particle hole transformations. It is compared to the same energies as computed by the NumPyMinimumEigensolver\n",
    "\n",
    "This notebook has been written to use the PYQUANTE chemistry driver. See the PYQUANTE chemistry driver readme if you need to install the external PyQuante2 library that this driver requires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing step 20 --- complete\n",
      "Distances:  [0.5   0.525 0.55  0.575 0.6   0.625 0.65  0.675 0.7   0.725 0.75  0.775\n",
      " 0.8   0.825 0.85  0.875 0.9   0.925 0.95  0.975 1.   ]\n",
      "Energies: [[[-1.05515973 -1.0759136  -1.09262986 -1.105918   -1.11628598\n",
      "   -1.12416089 -1.12990475 -1.13382618 -1.13618942 -1.13722134\n",
      "   -1.13711706 -1.13604434 -1.13414766 -1.13155119 -1.12836187\n",
      "   -1.12467174 -1.12056027 -1.11609624 -1.11133942 -1.10634211\n",
      "   -1.10115032]\n",
      "  [-1.05515974 -1.07591361 -1.09262987 -1.10591802 -1.11628599\n",
      "   -1.12416089 -1.12990476 -1.1338262  -1.13618944 -1.13722136\n",
      "   -1.13711707 -1.13604436 -1.13414767 -1.13155121 -1.12836188\n",
      "   -1.12467175 -1.12056028 -1.11609624 -1.11133943 -1.10634212\n",
      "   -1.10115034]]\n",
      "\n",
      " [[-1.05515972 -1.0759136  -1.09262986 -1.105918   -1.11628597\n",
      "   -1.12416089 -1.12990473 -1.13382619 -1.13618943 -1.13722135\n",
      "   -1.13711707 -1.13604434 -1.13414766 -1.13155119 -1.12836186\n",
      "   -1.12467174 -1.12056028 -1.11609624 -1.11133942 -1.10634211\n",
      "   -1.10115033]\n",
      "  [-1.05515974 -1.07591361 -1.09262987 -1.10591802 -1.11628599\n",
      "   -1.12416089 -1.12990476 -1.1338262  -1.13618944 -1.13722136\n",
      "   -1.13711707 -1.13604436 -1.13414767 -1.13155121 -1.12836188\n",
      "   -1.12467175 -1.12056028 -1.11609624 -1.11133943 -1.10634212\n",
      "   -1.10115034]]]\n",
      "Hartree-Fock energies: [-1.04299622 -1.0630621  -1.0790507  -1.09157046 -1.10112822 -1.10814997\n",
      " -1.11299652 -1.11597525 -1.11734902 -1.11734325 -1.11615145 -1.11393966\n",
      " -1.1108504  -1.10700581 -1.10251056 -1.09745432 -1.09191405 -1.08595588\n",
      " -1.07963694 -1.07300677 -1.06610866]\n",
      "VQE num evaluations: [[49. 53. 50. 50. 41. 47. 49. 47. 51. 46. 42. 57. 45. 49. 49. 50. 50. 46.\n",
      "  54. 56. 55.]\n",
      " [50. 52. 56. 50. 43. 50. 47. 48. 50. 46. 42. 57. 49. 49. 48. 50. 47. 49.\n",
      "  54. 56. 60.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pylab\n",
    "from qiskit import BasicAer\n",
    "from qiskit.aqua import aqua_globals, QuantumInstance\n",
    "from qiskit.aqua.algorithms import NumPyMinimumEigensolver, VQE\n",
    "from qiskit.aqua.components.optimizers import COBYLA\n",
    "from qiskit.chemistry.drivers import PyQuanteDriver, BasisType\n",
    "from qiskit.chemistry.core import Hamiltonian, TransformationType, QubitMappingType\n",
    "from qiskit.chemistry.components.initial_states import HartreeFock\n",
    "from qiskit.chemistry.components.variational_forms import UCCSD\n",
    "\n",
    "molecule = 'H .0 .0 -{0}; H .0 .0 {0}'\n",
    "algorithms = ['VQE', 'NumPyMinimumEigensolver']\n",
    "transformations = [TransformationType.FULL, TransformationType.PARTICLE_HOLE]\n",
    "\n",
    "start = 0.5  # Start distance\n",
    "by    = 0.5  # How much to increase distance by\n",
    "steps = 20   # Number of steps to increase by\n",
    "energies = np.empty([len(transformations), len(algorithms), steps+1])\n",
    "hf_energies = np.empty(steps+1)\n",
    "distances = np.empty(steps+1)\n",
    "eval_counts = np.empty([len(transformations), steps+1])\n",
    "\n",
    "aqua_globals.random_seed = 50\n",
    "\n",
    "print('Processing step __', end='')\n",
    "for i in range(steps+1):\n",
    "    print('\\b\\b{:2d}'.format(i), end='', flush=True)\n",
    "    d = start + i*by/steps\n",
    "    for j in range(len(algorithms)):\n",
    "        for k in range(len(transformations)):  \n",
    "            driver = PyQuanteDriver(atoms=molecule.format(d/2), basis=BasisType.BSTO3G)\n",
    "            qmolecule = driver.run()\n",
    "            operator =  Hamiltonian(transformation=transformations[k],\n",
    "                                    qubit_mapping=QubitMappingType.JORDAN_WIGNER,\n",
    "                                    two_qubit_reduction=False)\n",
    "            qubit_op, aux_ops = operator.run(qmolecule)\n",
    "            if algorithms[j] == 'NumPyMinimumEigensolver':\n",
    "                result = NumPyMinimumEigensolver(qubit_op).run()\n",
    "            else:\n",
    "                optimizer = COBYLA(maxiter=10000)\n",
    "                initial_state = HartreeFock(operator.molecule_info['num_orbitals'],\n",
    "                                        operator.molecule_info['num_particles'],\n",
    "                                        qubit_mapping=operator._qubit_mapping,\n",
    "                                        two_qubit_reduction=operator._two_qubit_reduction)\n",
    "                var_form = UCCSD(num_orbitals=operator.molecule_info['num_orbitals'],\n",
    "                                    num_particles=operator.molecule_info['num_particles'],\n",
    "                                    initial_state=initial_state,\n",
    "                                    qubit_mapping=operator._qubit_mapping,\n",
    "                                    two_qubit_reduction=operator._two_qubit_reduction)\n",
    "                algo = VQE(qubit_op, var_form, optimizer)\n",
    "                result = algo.run(QuantumInstance(BasicAer.get_backend('statevector_simulator'),\n",
    "                                    seed_simulator=aqua_globals.random_seed,\n",
    "                                    seed_transpiler=aqua_globals.random_seed))\n",
    "                eval_counts[k][i] = result.optimizer_evals\n",
    "            result = operator.process_algorithm_result(result)\n",
    "            energies[k][j][i] = result.energy\n",
    "            hf_energies[i] = result.hartree_fock_energy\n",
    "\n",
    "    distances[i] = d\n",
    "print(' --- complete')\n",
    "\n",
    "print('Distances: ', distances)\n",
    "print('Energies:', energies)\n",
    "print('Hartree-Fock energies:', hf_energies)\n",
    "print('VQE num evaluations:', eval_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, hf_energies, label='Hartree-Fock')\n",
    "for j in range(len(algorithms)):\n",
    "    for k in range(len(transformations)):\n",
    "        pylab.plot(distances, energies[k][j], label=algorithms[j]+' + '+transformations[k].value)\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('H2 Ground State Energy')\n",
    "pylab.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.plot(distances, np.subtract(hf_energies, energies[0][1]), label='Hartree-Fock')\n",
    "for k in range(len(transformations)):\n",
    "    pylab.plot(distances, np.subtract(energies[k][0], energies[k][1]), label='VQE + '+transformations[k].value)\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Energy')\n",
    "pylab.title('Energy difference from NumPyMinimumEigensolver')\n",
    "pylab.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VVREcHFynHsTbb7/tth7ExIkTueuuu5r76Txi5syZhIaGsm7dOkpKSnj++eeZMmUKBw4c4Pe//z1lZWUAvPrqq4wbN46VK1fyt7/9jZiYGNLT0xk5ciR79+5l+PDhnHfeefz5z39mypQpbNu2Dbvdzv33388333yDxWLhj3/8I7fffnud8ZcsWcKsWbOorKykX79+vPPOO3VqUtTnlVde4fPPP6e6upqPP/6YQYMGUVBQwA033MC+ffvo0qULb775JqmpqXX65ebmcsstt3DokJGm/cUXX2T8+PFePpsaTRuxazFUFLVJ7IMrnUtBNEV95dBUuwf4Uz0IJwcOHOCXX35h7969TJo0iT179hAfH8/SpUsJDQ1l9+7dzJgxo3ZZaMOGDWzbto0+ffpw4MABtm3bxqZNm2qP5eTNN9/kwIEDbNq0icDAQAoK6mZoz8vL4/HHH2fZsmWEhYXx9NNP8/zzz/Pwww0XOImNjWXDhg289tprPPvss7z11lvMmjWLESNGsHDhQpYvX861115bK4+TO++8k7vuuosJEyZw6NAhJk+eTFpaWqvOm0bTbmyeC+EJ0OfsNh22cymIRu70AcPmUHz4xPaonnD9ly0e1t/qQUyfPh2LxcKAAQPo27cv6enp9OnTh9tuu41NmzYREBDArl27avcfO3Ysffr0aVLWZcuWccsttxAYaPysunbtWmf7mjVr2LFjR+2dfFVVFWee2Xga9WnTpgEwatQo5s+fD8CqVav49NNPATjnnHPIz8+npKRuIahly5bVsdeUlJRw7NixRmcrGo1fUpYPu5fA6TdDQNtesjuXgmiKcx82bA7VLjUhvFDCz9/qQUi9ABsR4YUXXiAhIYHNmzfjcDgIDQ2t3R4WFuaxLI2hlOK8887jww8/9LhPSEgIAAEBAdTU1Hjcz+FwsGbNmjqfQ6M5Kdk+HxzVbeq95EQbqV1JnQ4Xv2zMGBDj+eKXW13Cz9/qQXz88cc4HA727t3Lvn37GDhwIMXFxfTo0QOLxcJ7772H3W532zciIoLS0lK328477zzeeOON2gt5/SWmM844g9WrV7Nnzx4AysrK6sxUPGXixIm8//77gKEUY2NjiYyMrLPP+eefzyuvvFL7vv4SlEZz0rD5Q0gYAt2HtPnQPlUQInJARLaKyCYRWWe2fWS+32Rud/vPdde3TUidDndtg9lFxrOX6rv6Uz2IXr16MXbsWC688EJef/11QkNDufXWW3n33XcZNmwY6enpDc4aunXrxvjx4xkyZAj33ntvnW033ngjvXr1IjU1lWHDhvHBBx/U2R4XF8ecOXOYMWMGqampnHnmmaSnpzdb/tmzZ7N+/XpSU1N54IEHePfdEx0IXn75ZdatW0dqaiqDBw/m9ddfb/Y4Gk27k7cbMte3uXHaiU/rQYjIAWC0UsqtM7uIPAcUK6UebW5fd+h6EE0zc+ZMpkyZ0mTsQmfgZPptaDop3z4Gq56Hu9MgortPhvDLehBiLIRPBzxfkO7g6HoQGo2mFocDtnwEfSf5TDk0ha+N1ApYIiIKeEMp9abLtolAjlJqdwv61iIiNwE3gbF0ommcpgzY7cFll13G/v3767Q15IGl0XQaDv1oeFWeO6vdRPC1gpiglMoUkXhgqYikK6WcbjszaHz20FjfWkzF8SYYS0zuDqSUOsFzR+M/LFiwoM3H7EildjUdlM0fQnA4DLqo3UTw6RKTUirTfD4KLADGAohIIDAN+Ki5fZtLaGgo+fn5+oKgqUUpRX5+vt+7wK5d9AbZs/vjmBVF9uz+rF30RnuLpGkrqm2w/TMYfAkEd2k3MXw2gxCRMMCilCo1X58POI3RvwbSlVIZLejbLJKTk8nIyCA3N7cl3TUdlNDQUJKTk9tbjAZZu+gNhqx/CKtUgUB3cola/xBrgTFTb25v8TS+Jv1LqCqF1KvaVQxfLjElAAvMpZ1A4AOl1Dfmtqupt7wkIonAW0qp3zTRt1kEBQV5FAWs0fgTPTc8YygHF6xSRc8Nz4BWEB2fzXMhMhl6T2xXMXymIJRS+4BhDWyb6aYtC/hNU301ms5AvMoFN2azeM+9vjUnK6U5sHc5jL8TLO0by6wjqTUaP+SoxDXQHtvGkmjanG2fgLK3W3CcK1pBaDR+yOGR92JTwXXabCqYwyPvbaCHpsOw+UNIHAFxA9tbEq0gNBp/ZMzUm9k26nFqlPEXLcXKtlGPawN1Rydnu1GzJrX9Zw+gFYRG47eMnvJHqk0zYUHsGK0cOgOb54IlEIZc3t6SAFpBaDR+S2lJYa0nU1RJ87Peak4yHHbY+jH0Pw/C3dug2hqtIDQaP6UwxyheleboSXRVNtiK2lkijU/Z/x2UHoFh7Rv74IpWEBqNn1KaZyiIH8WoEEjO9naURuNzNs+FkCg45cL2lqQWrSDaiy3zjBKns6ON5y3z2lsijZ9RUZAFwJHYcUZDzrZ2lEbjK9YueoOc2f1Qmz+ivLKStV+fWN+kvdAKoj3YMs8obVp8GFDG8+d3aCWhqUNN8REAIvqMoUCFU521pZ0l0ngbZ0qVBPIQgS5UMGT9Q36Td0sriPbg20fr1r0G4/23LUo3pemolGZToYLom5xImiOFmqyt7S2Rxss0mlLFD9AKoj0odpujsOF2Tack0JZLgaUrSV27kKZ6EVyw0/B00XQY4pX7JKL+klJFK4j2IKqBLKINtWs6JaGVeZQEdiU52kqaI4UAewXk721vsTRepECi3bYfoRulFdVtLM2JaAXRHpz7sBEM40qQ1WjXaEwiqvMpD44jNjyEPZbeRmOOXmbqKJQU5WHBjqNeqZpyFczT1dO54MUf+HFP+84ktIJoD4ZcDkFhEGgWrJEAuPhlSJ3evnJp/IoYRwFV1jgsFsEW1R87AZCtPZk6AsrhYPdbfyBSlbGm541kE4dDCdnEsX3U48y85T6CAy389q2fmb1oO7aq9lla9HXJUY079n8HlcVw5Rwoy4Ov7oHkMe0tlcaPqCg/RiRlqPAEAOJjIsmoSCZFx0J0CNYueImxx1aypu+fGXfd34HnAOhuPgC+umMiT3+TzpwfD/D9rlyemz6MEb1i2lROPYNoD1wDYlLGG20Hf2xfmTR+RUGO4bAQENkDgKRoKzscKToWogNwMG09Q7f8nW0hIxj7u8ca3M8aHMDsqafx/o2nU1Ft5/J//cizi3dSVeNoM1m1gmhrKo9B2ucw5DIICoW4QWDtqhWEpg4luUYUdUiMqSBirGysSoKSTCgvaE/RNK2govwYjo+vxyahdL/uXSwBAU32Gd8/lm/uOotpI5N5dcUeLv3natKzS9pAWh8rCBE5ICJbRWSTiKwz22aLSKbZtklEftNA3wtEZKeI7BGRB3wpZ5uS9jlUlx9P52uxQMo4OLiqfeXS+BXlBZkAhHdLAiA5xkqaSjE26lnEScvm/9xGH8dBMn71ArGJKR73iwwN4tkrh/Hva0dztLSCqa+s5l8r9zJ/fQbjn1pOnwe+ZPxTy1m4MdOr8raFDWKSUic49b6glHq2oQ4iEgD8EzgPyADWisgipdQOH8rZNmz+EKJToNcZx9tSxkP6F1CcCVFJ7Sebxm+oLjKiqKPiewLGElO6o5exMXsb9DmrvUTTtJCNi9/l9PwFrOn+W86YdEWLjnHe4ARG9jqLhxZu4+lv0hEBZXpBZRbZeHC+4eV26QjvXEf8dYlpLLBHKbVPKVUFzAUuaWeZWk9xJuz/HobNAHEpOJxi5trRy0waE0dpNjXKQte4RMBYYsolmorgrjpp30lI9qHd9PvpQXYHDmDk9S+06ljdwkN47ZqRxHQJqlUOTmzVdp5ZvLNVx3fF1wpCAUtEZL2I3OTSfpuIbBGRt0XEnVk+CTjs8j7DbDsBEblJRNaJyLrcXPdRiX7D1nmAOtGdtftQCImEg6vbRSyN/xFQdpQCia5do+4eGUqARci2DtCxECcZNdVVFL53LRbloMuMOQSHhLb6mCJCUbn7QLqsIpvb9pbgawUxQSk1ErgQ+LOInAX8C+gHDAeO4PTvaiFKqTeVUqOVUqPj4vyjyIZblDK8l3qeDt361d1mCTCWnPQMQmMSWpFLcUDX2veBARa6R4ayL6APHE0He007SqdpDuvevZ9Tq3eQPvoRkvoN8dpxE6OtzWpvCT5VEEqpTPP5KLAAGKuUylFK2ZVSDuDfGMtJ9ckEerq8TzbbTl6ObIbcdEhtoBhIyjjI2wnH/HwWpGkTwqrzKAuOrdOWFGNlu70n2Cshf3c7SaZpDttWf87Yw++wNvpCRl/s3ZKx904eiDWorheUNSiAeycP9NoYPlMQIhImIhHO18D5wDYR6eGy22WAO5eMtcAAEekjIsHA1cAiX8naJmyeCwHBcNpl7renTDCeD+lZhAai7UYUtSvJ0VbW2gybhI6o9n8Kc48Qv/QOMgISGfyH171+/EtHJPHktKEkRVsRDEeGJ6cN9ZqBGnzrxZQALBDDGBsIfKCU+kZE3hOR4Rj2iQPAzQAikgi8pZT6jVKqRkRuAxYDAcDbSqmT1zJnr4Ztn8ApF0CXru736TEMgrrAgdUw+OS3x2taTk11FTGqBHtYQp32pBgrXx2LRYUGITnbgCvbR0BNkyiHg0NvX8epqoTDl75PWIT7pHyt5dIRSV5VCPXxmYJQSu0Dhrlp/30D+2cBv3F5/xXwla/ka1P2LoeyXBh2dcP7BAYb6Ta0HaLTU3A0k3hRWCK612lPirZS4QiguuspBOtYCL/m57lPcIbtZ9YMup8zUse1tzgtxl/dXDsWm+ca0dL9z2t8v94TjCAoW2HbyKXxS4qPGg58QdE96rQnxRjGx+LIgXqJyY/Zs3kVI3e+wMYu4zj9qpM7xlcrCF9jK4L0L2HoFcYsoTFSxgEKDq1pE9E0/klZvuGPEdat7tJBckwXAI6E9oNj2UaiR41fcaykkJCFf6RIouhzwzuI5eS+xJ7c0p8M7PjM8DpJbWR5yUnSaMOQreMhOjWVhVnA8ShqJz2iDP/5vc7aENk6HsJfWLvoDbJn9yfsud4kO7LYk3gJ0bHdm+7o52gF4Wu2fATdBkDSyKb3DQo1lMQBrSA6M47SbAC6xtetMBgaFEBcRAhba0zFoe0QfsHaRW8wZP1DdCcXESNJwojM91m76I32Fq3VaAXhSwoPGrOBYVfXTa3RGCnjjJiJylLfyqbxWyxlORQS4TbiNinayq5joRDeXafc8BN6bngGq1TVabNKFT03PNNOEnkPrSB8yZZ5xnNzKsX1Hg/KDod/9o1MGr8n2JZLoaWb221JMVYyCsuh+xBtqPYT4pX74Nb4E3KUnnxoBeErlDIyt/aeCNG9PO+XPNYoQardXTstYVV5HAt2ryCSY6xkFVWg4ocYkfk1VW7307QdR8V9ip+jEuu2/WRCKwhfkbEOCvY2nFqjIULCIXGEVhCdmKiafCpC3F90kqOtVNkdlEQPBEc15O1qY+k09Tk88h4c9bOqqmAOj7y3fQTyIlpB+IotcyEwtGVR0SnjIHM9VHsvK6Pm5MBht9NVFWHvEu92uzMWIjPETPioDdXtzpgx47AIFKpwHErIJo5tox5nzFTv5l5qD7SC8AU1VbDtUxg0BUIjm9+/9wSwVxmzEE2noig/myCxIxHuXSSToo1YiH2OHhAQohWEH6B2LQbgtcHvY3mkiO6z93QI5QBaQfiG3UuMaOjGUms0Rs/TAdHxEJ2QoqMZwIlR1E6cM4jDxdUQP0gbqv2A6vTFbHb0pVev3u0titfRCsIXbP4QwuKh76SW9bdGG14qWkF0Oo7lGQrC2tV9ArbwkECiuwSRWVQOCUP1DKK9KS8gKHs9KxzDObVHC1YL/BytIFrBwo2ZJxYMLy+AXYsN19aAVuRCTJkAh9dqL5VORoUZRR0Zl9zgPknRVjILbcZNRFkulOa0lXia+uxZhigHK+zDGdg9or2l8TpaQbSQhRszeXD+VjKLbCiOFwzf9M07hndJc72X6pMyDmpskLXRK/JqTg7sJWYUdfeeDe6TFG0ls8gGCacZDboEafuxazElATEURp9GRGhQe0vjdbSCaCHPLN6Jrdpep81WbSdw61yIP82oM90aUswUwXqZqVMhx3IoVVa6hDW8XKK0mPoAACAASURBVJEUY8wgVIJZvlJHVLcP9hrYs4wfZQSDevim3kN745GCEJF+IhJivj5bRO4QkY55RjzEXWHw3nKEIWoXDLvK89QaDREWC3GDtILoZATbjlJkiWl0n+SYLpRV2SlS4RCZpA3V7UXGWqgo4nPbkA5pfwDPZxCfAnYR6Q+8iVEv+gOfSXUS4K4w+GUBq7BjgaHNSK3RGCnj4NDPukB9J8JamUdJkPsoaidJ5m/PWGYaog3V7cXuxSgJ5Ht7aqdXEA6lVA1GDelXlFL3Au798FwQkQMislVENonIOrPtGRFJF5EtIrKgoZmIu77+xL2TB2JxmSQIDi4PWEVe3JkQ2eSp8YyU8VBVCtlbvHM8jd8T0UgUtZNk09U1w2moztsFNZVtIZ7GlV1LOBozglK6MLiTK4hqEZkBXAd8YbZ5apGZpJQarpQabb5fCgxRSqUCu4AHm9HXbzh7oPEnDgsJQIDRsotkySVhwnXeGyRlvPGs0250CpTDQVdHIdUNRFE7qTuDOA0cNUZeJk3bUXQYjm5nU+hYwoIDapV2R8NTBXE9cCbwhFJqv4j0Ad5ryYBKqSXmbARgDdCwP58f8/mWIzgUzLv5TPY/dRF3xq2jXIVQ3Huy9waJ7AFd+2oF0Uk4VlpEF6mE8IRG94vuEkSX4AAjq2uC6QyhDdVty56lAHxTNYxBPSKxWFppc/RTPFIQSqkdSqk7lFIfmu/3K6We9qQrsERE1ovITW623wB83cK+AIjITSKyTkTW5ea6T7vrCxZsyGBgQoQxtay2cabtB75ynM6ctV6WIWUcHPoRHA7vHlfjdxTmHAIgMKrxJUoRIdn0ZKJbPwi0akN1W7NrCSo6hWV5UZzao+PFPzjx1ItpvIgsFZFdIrJPRPaLyD4Puk5QSo0ELgT+LCJnuRzz/4Aa4P3m9nVFKfWmUmq0Ump0XFzja7feYn9eGRsOFTFtZBIiAju/JqC6lANJF/P26v2UVlR7b7CU8Ubajtw07x1T45eU5hq1qENjEpvctzYWwhIA8afqWIi2pLoC9n9HWa9zKK2wd1gDNXi+xPQf4HlgAjAGGG0+N4pSKtN8PgosAMYCiMhMYApwjVJKNaevP7BgYyYWgatD18ALQ+CT60ECuPKUQIpt1fxvzSHvDea0Q+gypB0eW6GhICJim151TYoxFQQcLx7k/q+k8TYHVkF1ObuijFglrSCgWCn1tVLqqFIq3/lorIOIhIlIhPM1cD6wTUQuAO4DpiqlypvT10NZfYrDoZi/IYN7emwmatlfoPiwsUHZSfnpr9yfuIW3fthHeZWXXFOje0Fkso6H6ATUFB8BIDqh6QJTSdFdKCqv5lhljeHqaiuA0iO+FlEDsHsxBFr5yX4qIjAwoZMvMQErTPfUM0VkpPPRRJ8EYJWIbAZ+Ab5USn0DvApEAEtNF9bXAUQkUUS+aqJvu7PuYCEZhTZm2v57Yr2Gahs3VL5HflkVH/zspVmEiFGG9OCP+g6xo1OaQ6UKIjK68TgIcKkLUWjGQoA2VLcFShm51vr+im1Hq0jp2oWwkFbkXPNzPP1kp5vPru6mCjinoQ5KqX3AMDft/RvYPwv4TWN9/YEFGzPoEhyA1ZbtdntIWRZn9u3Gm9/v43dnpBAaFND6QVPGwZaPIH8PxA5o/fE0fklgeQ75lhgSLU3ftzndKjOLyhmYYuZkyt4KA87zpYiavF1QdBDG30n6d6UdenkJPPdimuTm0aBy6KhUVNv5YssRLhjSHYlqYJ04Kpnbz+3P0dJK5q077J2Ba+Mh9DJTRya0Mo+SgKZnD2CUHgVzBmGNhqheOqK6LTCLA9l6n8uB/DKtIABEJEpEnne6k4rIcyIS5Wvh/I1v045SWlHD5SOToY8bp6ogK5z7MGf27cbolBheX7mXqhovuKd262/Ul9CG6g5NRHU+5SGeFbqPDQ8hOMBCRn1Dtca37F4C8aeRZotCqY5toAbPbRBvA6XAdPNRArzjK6H8lfkbMugeGcoZ4TlGSdH4wRCVDAhE9YSLX4bU6YgIt587gKziCj7dkNH6gUWMZaaDq7UdogMT4yigyuqZq7bFIiRGhxozCDAiqvN36zrmvqSiGA79BKecT9qREoAOHQMBntsg+imlLnd5/4iIbPKFQP5K3rFKVu7K5U/jEgn49A8QEgnXfgbh7tMinDUgltTkKF5buYcrRyUTGNDKzOq9J8COhcb6Z0zv1h1L43dUlB8jkjJUWONR1K4kxViNfExgGKqVw0i5kTjCR1J2cvauMNKaDJhM2sYSIkIDa9OedFQ8vWrZRGSC842IjAc61a3K55uzsDsUN5a/aQStXfZ6g8oBjGjX288ZwOECG59tymq9ALX1IXTajY5IQY4x0wxoIoraleToLi6xEGbKDb3M5Dt2L4HQaEgeQ/qRUk7tHmkEynZgPFUQfwL+aWZYPYjhqnqL78TyP+ZvyOTm2C1E73gfxt8J/c9tss+vT43n1B6R/HPFHuyOVi4NxZ0K1hhtqO6glOQaDg0h0U1HUTtJirGSW1pJRbUdYvpAUJg2VPsKh8NQEP3PxSEBpGeXdvjlJfDci2mTUmoYkAoMVUqNUEpt9q1o/sPunFIKs/Zwd8WrkDQazvmbR/2MWUR/9uWV8eXWVgYxWSzQa5w2VHdQyguMKOqw2CSP+ziXN44UVxi/j4TBegbhK45sNOp/D5hMRqGNY5U1Hd5ADU3YIETkd0qp/4nI3fXaAVBKPe9D2fyGhesP8nLQqwRZBK74DwR4Xnv2gtO6MyA+nFeX72bK0B6ty/qYMg52fgklWRDp+Z2mxqgh/szinWQV2UiMtnLv5IFcOsLzi7GvqS4ybiCi4j1PbuwaLNcnNsywQ2yfbzgydPCljzZn1xJAoP+v2bHfaaDu+AqiqRlEmPkc4eYR7kO5/AaHQxG//jlGWnZjmfpSsw3EFotw2zn92ZVzjCU73AfXeUxvXR+iJSzcmMmD87eSWWRDYdRReHD+VhZuzGxv0WpxlGZToyx0jWv+DCKj0MxYk3Ca4WlT4j+fq8OwezEkj4GwbqQdKcEicEoHTrHhpFEFoZR6w3y5TCn1iOsD+Nb34rU/aasX8fuaBRxKuRyGXN50BzdcNLQHvbt14ZXle2ggN6FnJAyF4Ahth2gmzyzeia3aXqfNVm3nmcU720miEwkoO0qBRBMQ4HnkfY+oUAIsog3VvqY0B7I2winnA5B2pITesWFYg72QJcHP8dRI/YqHbR2LY7n0/O7/sZ9E4qe/2OLDBAZYuHVSf7ZnlbBi59GWyxMQCL1O1zOIZpJV5N7hrqH29iC0IpfigK7N6hMYYKF7ZL1YCNCpv72NWRyIAUYxMMNA3fGXl6AJBWEm5/sLECcid7s8ZgMdW306HNgX3EJIdSmLTnmC0LDW/SAuG5FEcoyVl79t5SwiZbzh616W1yp5OhOJ0aENtPuPD3tYdR5lwZ5FUbuSFG09Hk0dEmEsgeoZhHfZvQQiEqH7UEorqjlUUN5ha1DXp6kZRDCGrSGQuvaHEuAK34rWzqx5jYC9y3is5neMH/erVh8uKMDCn87ux6bDRaza04qLu65T3WyuHN3zhDZrUAD3Th7YDtK4J9peQGVo8wteJTkryzlJGKJdXb2JvdoIkBtwHoiwM7sU6PgR1E6askF8Z9obzqhng3heKbW7jWRsezI3wLLZrLWOZ2XExYxOifHKYa8YlUz3yFBe+XZPyw+SOMIoMantEB6Td6ySQAvEhgcDRk3nJ6cN9RsvpprqKmJUCSqs4cDLhkiKtpJdUkGN3cz5lTAE8vdCVZmXpeykHPoJKktgwHH7A8Cg7noG4Uq5WQ/iKxFZ7nz4VLL2oqIEPrkBe1g8NxVdx7SRyV4rSB4SGMDNv+rLLwcKWLOv0XpLDRMYDD3HaAXhIZU1Rgbe3wxNZO3//ZrukaGc2beb3ygHgIKjmVhEIZHdm903OcaK3aHILqkwGroPARQcTfeukJ2VXYshIBj6ng3AjiOlRFmD6BHlftmyo+GpgngfSAf6AI8AB4C1PpKp/VAKvvwLFB3kiwGPUqjCuWyk537pnjBjbC9iw0N4ZXkrJmAp4411Zluh9wTroKxIz6WovLq2fvikQXH8sDvPO1l2vURxrpFmI7gZUdRO6hQOApfiQdpQ7RV2LzH+byGGV396dgmn9ojo8Ck2nHiqILoppf4DVJvLTjfQSLGgk5bNH8LWeXD2g/xrXzwjekUbAUheJDQogJvO6sPqPfmsP9jCC3zKeEDBoZ+9KltHZP6GDOIiQpjQ3zAATxoYz7HKGtYdKGhnyY5Tlm/ELXTp2gIF4awL4TRUR6cYrtDaUN16CvYbBYJOMbyXHA7Fzk7kwQSeK4hq8/mIiFwkIiOAJn3yzNxNW83SouvMtq4islREdpvPbhf4ReQ6c5/dInKdh3I2ny3z4IUhMDsaFt4KsQPZ0e+PpGeXMs3Lswcn15yeQpcgC7/99xr6PPAl459a3qygrfWbN6EA9cFVZM/uz9pFbzTZx2u4nq8Xhhjv/ZTCsipW7DzKJcMSCdz+CbwwhPM+HsTqkDvIWf1ee4tXS1WhkcwxKv5EY3pTJEbXm0FYLIa760lqqF676A2yZ/fHMSuq2b/thRszGf/U8hb9p9yye4nxbNofDhaUU15l1wrCDY+bBYL+AtwDvAXc5WHfSUqp4UopZ7nSB4BvlVIDMILtHqjfQUS6ArMwSp2OBWY1pEhaxZZ58PkdUHwYo4KqgqKD7Px2DkEBwpShnmfWbA5Ld+RQZVdU1jiaHdm7dtEbDN74KIKRTaE7uQxZ/1DbKIn656v4sPHeT5XEF1uyqLYrZkasrZVbUCRJHhfuf9Jv5LaXGBH2XROaryBCgwKIDQ85nvYbTAWx/aSrHbJ20RsMWf8Q3cnF0szftk+i5XctNop1desHHDdQn9pJDNTgYT0IpdQX5stiYFIrx7wEONt8/S6wEri/3j6TgaVKqQIAEVkKXAB82Mqx6/LtoycWWKmp4Iz9r3LOoI+ICQv26nBOnlm8k5p62V1t1XYe/mwbWcWNB29duuEfWKWqTptVqui54RmYerPXZa2Du/NVbTPaU6f7duwW8OmGTAZ1jyB5w/0nyB1KJTVLHyHQD+S2lOVQSAQxIS0zfCbHWI8vMYFhqF73Hyg6BDEpXpLS9/Tc8Izb33bShn/wWmTj2ZNfX7m3wWj5FjkkVJXBgVUw5sbaprQjJQRYhAEJnSLLEOChghCRdzBusetg2iIaQwFLREQBbyil3gQSlFLO1KbZgLsKKUmAa0HnDLPNnWw3ATcB9OrVqwlx6lHsvtpbgsr32fISNBzBW1JRwz++aTz9wy0heeDGPhav2iBwroHz1WB7O7Iv9xibDhfx198MguXu5Qso9Y+cRcG2oxRZutLSKXJSjJUdWSXHGxLMlBs5204qBRGvct3+trur/Cb/Fw3R4mj5/d+DvbI2vQZA2pFS+saGERrUsWOEXfG0otwXLq9DgcsAT6rgTFBKZYpIPLBUROr43imllKk8WoypdN4EGD16dPOOFZVsLpfUJVu6MWlg833SPSUxut4dn7M9KpTl95zdaN+cJ+LoQe4J7UclluY7STaTBs6XUXbVv1iwMROLwCXDk2BNNyg/UYHmB8TR/Nhl7xNWlcexoG4t7p8cbWXpjhwcDmW4ZCcMBsQwVA+6yHuC+pDNh4uIJZYkTvyeciSW9McuaLT/Oc+uJKu44oT2FkfL71oMweFGin2TtCMljPJSTNTJgqf1ID51ebyPUZd6tAf9Ms3no8ACDHtCjoj0ADCf3SUnygRcF2STzTbvcu7DEFT3B2RTwazu9WeCA1tZIrQR7p08EGu9uxBrUAD3XTCI0KCARh8ZI+/FpuoufdlUMIdH3uszeWs592HDJ9yVIKvR7kc4HIr5GzKZMCCOBArN5aW6t6ZVllD+XjWd8qqa9hHShciaAipaEEXtJCnGSlWNg7yySqMhOAy69j0pXF2rahw8v2Qn0/71I9ul3wnbbSqYjJH3Nvm/uO+CQW7+U5aWRcsrZRio+55txB0BxbZqMotsncpADZ4bqeszAGj0FltEwkQkwvkaOB/YBiwCnF5J1wGfuem+GDhfRGJM4/T5Zpt3SZ0OF78MUT0Boczag/urb6Tfr6/3+lCuXDoiiSenDSUp2opguCp6Gtk7ZurNbBv1ONnEoRTUKAvbRj3OGF/bH8A4X73Pos7FdvQf/c7+sPZAAZlFNqYN7w4LbgIUnPeIkU8HICSS/Wf+nfnV41i9p4UBi15CORx0VYXUdPG8FnV9jqf9rmeo9nNX153ZpVz22mpeXr6Hu07J5zxZS37McLKJw2miy0s6x6Pftut/ysn14/u0zP6Qs91ImW66twKkOw3UnSTFhhNPbRClGPYEMZ+zOdGwXJ8EYIEZUBIIfKCU+kZE1gLzROQPwEGM2QgiMhq4RSl1o1KqQEQe43gw3qNOg7XXSZ1ee4H7w5s/kRNQyYie0T4ZypVLRyS1OJp3zNSbYerNLPzXX7k055+MmXSJl6VrhKpS6DkWZn4JL4+Ewz/5XYGa+RsyCQsO4DfFHxpryVNfhZG/N0rFvjcN8nbT5+zrCF/1LcvTj3Le4JZfnFtLUX4OMWJHIlouQ3JMF8BwdR3Zy1wC6T4U0hZB5bHaIC9/we5QvPXDPp5bsotIayBvX9WPc1bcB9EpdLvlCyPpIMD/Lqdn1lrDYBzcdDyS8z9VUW1nzOPLyCmpbJmAu8170QGu9ofOUyTIFU+XmCKUUpEuz6copT5tos8+pdQw83GaUuoJsz1fKXWuUmqAUurXzgu/UmqdUupGl/5vK6X6m493WvMhPSGjsJw1+wqYNiLppImSdCSOBKD8wC9tM6C9Go5sgaRRRlW9Cf8PMtbC/u/aZnwPqKi289XWI9zSN4/g75+GIVfAiN8d32HY1VB8iODMn5nQP5aVO4+2LrtuKyk6egiAoOiWu1TXRlMX1UvaB3B0R4uP6wsO5pdx9Zs/8eTX6ZwzKJ7Fd07knJ2PwbEcuOLt48oB4Kz7oDwf1s9p1hihQQFclNqDb7YdadkS4q4l0GMYRBy36qVnl9I1LJj4iJDmH+8kpql03yMbe7SVkG3BZ5sMm7s/5ehpiqg+o6lRFkr2tJGCOJoGNTYwFRMjfgcRPeC7Z9pmfA9YuiMHqSziptwnILonTHmh7uxm0EWG8XHzh5wzKJ4jxRWkHSltN3mP5RmmNWvXlv/uwkMCibIG1c3q2t1UENn+YYdQSvG/NQe58KUfSM8u5YWrhvGv342kW9p7kP4F/HoWJNW7pPQ6HXpPhNUvQ/WJBujGuGxEEmVVdpZsz2meoOUFkPFLbe0HJ2lHOleKDSdNzSCea+TxrG9FazuUUny6IYOxfbrSs2uX9hbHY/omxrFL9YSs9W0zYNYG49n5Rw4MMZZtDq7ym/Tj89cf5qUubxNsOwqXvw2h9ZYEgsNg8CWw4zPO7mcsvbSqiFMrqTCjqCNjW+cJllTfMy6qJ4RE+UVEdXZxBde9s5aHFm5jVEoMS+46i8tGJCM522Hx/0H/8+CMP7vv/Kv74Fg2bGxe5PuY3l1JjrHy6YZmumDv+RaUo479we5Q7Mwp7VQBck4atUEopVobFOf3LNyYyRNfppF7rJKCY1Us3Jh50swiesZYmU8/Li5Y1zZ2gMz1EBpteMg4GXkd/PAcfPcPuHZho90XbszkmcU7ySqykRht5d7JA716rnNLK0naN5dJgWvgvEcheZT7HVOvgk3vE5+1nKFJCaxIP8qfJ/X3mhzNoabYCAnq2r35UdSuJMdYOZDvkuJ768dQUwHr3obdSw1Ps2Y4E6xd9AY9NzxDvMrlqMRxeOS9HjtCuH7P0V2CsFXVIGLhsUuH8LvTexl34VVl8Mn1YI2GS/9lpAhxR++J0PMMWPWi8VsL9Cx41WIRLhuRxD9X7CGnpIKESA+CELfMg8/vNF5/fB2cOwtSp7M/r4yKakensz9AM7yYRGSIiEwXkWudD18K1hY4w/NzjxnGrCJbtd8Vs2+MwAALWWGDsdpLoGCf7wfM3GjMHlwVUXAXGHc77FsBGesa7OqTVAj1+GH19zwU8B5lPX8FZ97e8I69J0JkMmyey6RB8Ww4VEhhWVXD+/sQOZbDMWWlS3hUq47jLByklDqeEsVuGmmLD8OiO2DdHLAVNfnYuOAFtykvVi94jWJbdaOPD345yAPzt9R+z4Xl1VTaFXefdwq/PyPl+BLN1/dD3m6Y9iaEN+LiKwK/uhdKMmDzB806J5eNSMKh4LNNHvzGnOesutw8Zxm1aWRqa0B0Mg8m8NyLaRZGeozBwFfAhcAq4L8+k6wNaKyY/ckyi7DFDYNDGEXVu53oR+41qsoNg+cpblJwjf6DcYf3/TPw24/cdvf5ua4qZ9Tau7FZwoi56q2G70jB2JZ6Jax+mfPH/J2XFXy/O9cIqmtjgm1HKbDE0Fo/o6RoK2VVdopt1US7TSFjgy/uNB5NMAJOiGi2ShUpG59j2M/Nj8xWCub8eIA/nmXOPLd9aiwZTbi7ts5Co/Q717B7/fA8DL/GcJDwgL5x4YzoFc38DZncdFYT/41G0sikD/6EQIvQP96/vMHaAk8jqa8AhgEblVLXi0gC8D/fidU2nAzF7JsiPHkotoPBBB1aS+BQH1aBzd4Cym54MNUnJBzOvBWWPw5HNhseIPXw9bkuWnAPKfZDLB75LyaHexAFn3o1rHqBwXlL6BZ2CsvTj7aLgrBW5lHaiihqJ8kxx2MhohtLfTL5ySaPpb550O1qZaLk87cpgxvt+9gX7r2mar/ngv3w+f+D5LEw6a9NygKYs4j74MOrYesnMHyGZ/2AaSOS+Ntn29mRVcLgxEaWiBpJI5N2pJT+8eGEBHaeFBtOPFUQNqWUQ0RqRCQSI/q5dYumfkCDKS/8qJh9U/TrEc121ZtTD63z+MtsEZmmIby+p4mTsTfB6leMWcRVJ947xEaEkFt6ol+6V8719gVEp73PG/apXH7O5Z71iR8EiSOwbJnLrwa+wvL0o9gdigAvVQ/0lIiafLLDG7/oekJStBkLUWRjSIMpUXoairwJchY/T3c36VxsEsIfxsY3GpPw9qr9Df+n7NXw6R8Agcvf8ngmAMApFxg5pn541rClWDy7WE9JTeTRL3Ywf0MGgxMbOM8Ou5EhwO4mbiIqmbQjJZzRt/VK/GTEUxvEOhGJBv4NrAc2AD/5TKo2oqGUF/5UzL4p+seHs9nRj5C8bWD3YdqIzA0QmVTHN7wOoVFw+s2Q9jnk1L2LLKusaTD19MxxvVsnV+FB1KI72CYDWN/3VmLDm+Gnnno1ZG/hkh5FFJVXs/FQ21boUw4HXR2F1Fhbn/fLdQbhLoVMc1KibBp4B+X10rnUKAthVMDrExotVNXof2r5Y8aNxtSXm59EUATOugfy98D2BR53iwkLZtLAeD7bnHW8bnd9fnjOUA5u0siUTfwrR4orOl0EtRNPA+VuVUoVKaVeB84DrlNK+TYfRRvQmpQX/kLvbmFspR+B9grITfPdQJnrIXFE4/uc8ScjxuCHuh7QsxZtJ6+silsn9as91wmRIViDLCzanNXy8p/mHand4eCWij9zyajezes/5HKwBHLGsWUEWITl6W3r7nqstIguUolqRRS1k+guQXQJDjBiIeqlkCGqp/HeQy+mNWHn8teaGzlCHA4lZBPHxlFPwXVfGDch71wAS2dBzYl33A3+pyLSYfVLMGomnHZpyz7kqVMhbhB8/yw4PP/NTBuZTG5pJav2uMl4fPAnWPkkDL0SLvnnCedsc4wRTT2oE7q4gudG6kXAXOAzpdQBn0rUxrQm5YU/EBxoIS9qCJRhXMS7D/X+IOUFULgfRjbhuNalq5E/f/VLcPaDEDuAhRsz+WR9Bref05+/nD+Q+yYPqt198fZsbn5vPf/4Jp2HmljbdsuKv0PGWt5PmkVxdSLnntrMO/HwOOj/a0J2fMKYXhewPP0o910wqOl+XqIw5xARQGBk6wtTiYgZC2F64bikkGkO1XYHn2/O4ozBV9LjGsNe0d18APCn1bD4r7D6RcN99rLXoUdqnWOc8J8qzYHXb4a4Uz2ygTSIxQIT74H5NxrBdYOnetRt0qA4oqxBzN+QydmuWZrLC+DTG40yrRc9b8TM1Dtnaav2A50vxYYTT5eYngMmADtE5BMRuUJEWlbdRON1whL6U0K4sQzkC+oHyDXGmbdBYCj88DwH8sr4vwVbGZ0Sw53nDjhh18mndefaM1N4a9V+VjT37n3vClj1AtXDfs/ThwczJbVHy/L0D7saSo/wu4RDpGeXcqSJgk3epDTXcL8MbUEtanck1S8c1AK+35VLflkVlzV00xQaCZe8Cr+dZ6RQ//c5ht2poeVNhwMW3AyVpXDlO4ZbdGsYMg269jPG9DBFSkhgABcP68GSHdmUVpjVk5WCRbcfT/FRP6DSJP1ICbHhIcR1shQbTjxdYvpOKXUr0Bd4AyPBXvuFn2rqMCAhkk2OvjgyfRRRnbnReG5qiQmMu/LRN6C2fMQT739FYICFl2aMIDDA/U/tr785lUHdI/jLx5vJKfEwncKxXOOiE3sKXyXdSXmVveUFnk65EEKiOMu2DIAV6ScaZ32FrdBQEBGtjKJ2khRtrZtuowXM35BJ17BgfjWwifTjp0yGW9fAqVMM77W3JxtxDfX58WUjRuaCJyH+1FbJBhjG6Yl/Mbzqdnme4HnayGQqqh18vc0o78ratxpO8eFCWnZJp7U/QPMC5azA5cAtwBiMcqEaP8AwVPdFjqYZ8QreJnM9dBtgGKI9Ydzt2AlgUu77PH15ap0UzPUJDQrg1d+OwFZl566PNmF3NHBXuGUevDAEZkfDi0OgLA+ufIePtxTQs6uV0S0t5BIUCqddSsT+bxgQTZvaIZxR1DHx3lEQyTFdKCyvNpwCGNC+1AAAIABJREFUWkCxrZqlaTlMHZZIUAMKvQ5dusKVc+Dy/xjG49cnwprXYfNHx7+rZbOMGIZRXjRZpk6H6F7NmkWM6BlNn9gw5m/IMNKgL/4/6P/rhlN8ADV2B7tyjjG4ky4vgYcKQkTmAWnAOcCrQD+lVCOhqpq2xOnJJMpu3Fl5E6UMBeEu/qEBVhwJ4IPqXzE96AcuSK5ucv/+8RHMnjqYH/fm8/p3e0/cwRnlWnwYUEYKCYuFov2bWL03z8jr05o0I8NmINVl3Byfxuo9eVTUC+jzGaU5VKogImNaXizIFbdZXZvBV1uPUFXjYNrIZtrkhl5hzCZ6T4Bv7oeFtxz/rsBI8rj14xbJ5JaAIJhwF2SuM2YnHiBipN7YvC+L6nkzzRQfrzcaULkvr4yqGkenjKB24ukM4j8YSuEWpdQKpVQL3U40vqBfXDhblBml6m07REkmlB31zP4AHC2p4J55m1na9WoCRIwlBg+YPronFw9L5Pmlu1h/sF7pD3dRrvZqAlY8hlJGMFSr6HUGRKdwTuW32Krt/LzfN6VH6hNYnkOBRCONRX03A+dMraXLTPM3ZNA/PpyhSS1I+xHZA675GKwxRrI7V2qMiGSvMvwaw+26GZmELxuRxKzA/xJYsAcue6PxFB903hoQrjSV7vs+AKXUYmBavW1/96FcmmZgDQ4gJCaRwsC44wZlb+FUOB7MIBwOxV3zNlFWVcPD10xGhs+A9e9CaXaTfUWEJy4bQmJ0KHd8uInicpeZRwNRrmGV2YxKiaF3bNPFZJoYHIZdTUzOT/QKKmq+wbyFhFbmURLovQCs2liIFswgDuWXs/ZAIZe1ph6KiJHPyR2NRXe3BGcm4UM/woFVHnXpmfU1Vweu5MPgy1F9z25y/7QjpQQHWOgX1/lSbDhp6tblapfXD9bb1ngVcU2bMiA+gh3S/3jEs7fIXA+WwOMFaBrhX9/tZfWefGZffBoDEiKMXDuOGvjxFY+GigwN4pUZI8kpqeCB+VuMxHOHfm4wajbL0a1hb5vmknoVguK22I0sT2+bIkIR1fmUh8R67Xhx4SEEB1haNINYsDETES/UQ4lqwJ7SUHtrGHkthMUbmYSbovAAfP7/yItO5eGSqWzJKG6yS9qREvrHh3tmj+mgNPXJpYHX7t67P4BIgIhsFJEvzPc/iMgm85ElIm5zRIuI3WW/RZ6M1ZnpHx/OTxW9jayu5V5cIsnaYCiHoMa9mtcfLOT5pbu4KLUHV40xs7B07WMYFNe9bRiVPWB4z2jumTyQb7cdZsd7dxtBWSGREFDXzbBKQnnecTVTUlsfQwAYiQ6Tx3J+9XIOFZSxN7es6T6tJNpRQJUXoqidWCxCYnQoGYXNc1RQSrFgYwZn9OnWqEOBR7QyirtZBFmNTML7v4PDjRTNslfDJ0aKj+Cr52AJDDaM1U1gFAnqvMtL0LSCUA28dve+Ie7EMHAbnZSaqJQarpQajpGuY34D/WzO/ZRSnkXEdGL6x4ezwd7HeJO10TsHdTgga1OT9odiWzV3fLiRHlGhPDltaN0ligl3G/aDn171eNibBvz/9s48Pq7qyvPfo9Ku0mpLsi05trGEjW3wjklIgJAESADHQDqBBBKSpkN60p2lOybNdHdImMw0+fCZT+j0TE8gCSGTEBgChhBCWILtsHuTd2zjFduSLclarMXa68wf98kuSSWpJFVJVNX5fj710av37nvv3KpSnbr33N85Lfw5+/vMP/QwjXM+B9/a4dbeeypXzS3lB3yVM3NuJC8zvPoAYbHwZvJaDjJf3ov6NFN7Wyu5tBLIipyDgNFpISqONnKk7szIg9OhGKOKe8Qs+wpkFLgVTYOx9ocuoL3y38mZMptPzCvmDztODKngr2vpoKa5I6GXuMLwDmKhiDSJSDNwkbfd+3xYya6IlALXAj8PcSwHtypq6CozRliUFfnZGfAC1ZGKQ9QdgI6mIeMPqsrda3ZQ3dTOT25ZTE56vwRshefD/Btg48+GH9n0dMOr95P0849RktrKN5P+K39VdQttkum+YL69C77fyPpPruPRtksi84UWzPwbwJfKX2e/HfUqc/XV7hesLwIq6mBGo4VYU3Gc9JQkPnlhhGwJeq/49q7oOQfwMgl/Hfa/FPqH0YFXnOp76e3u/cUtaqhv7eQv7w6ueektQ2sjiCFQVZ+q5qhqtqome9u9z8NJxfgAcBcQylWvAl5R1aZBzk0Xkc0i8raIDJq8RUS+6rXbXFs7fiKn9xtlRX6ayaQhY0bkVjL1xjOmDT6CeGzjMZ7feZJ/vGoOSz4wiBbhsu9AZwtseHDwe53a78RWa38IF1yH7+sbuOnmr7C/poV7+6WQfqriOPmZKX3TJkSCzAI4/2quDrzGlsO151S3UaCp1mVbTcuPjIq6l5K8TGqaO+joDm+pbkd3D8/tOMHV86fgT4tqPuDocfFXnUbn1X5VkFtq4OmvDUjxcdn5hUzKGnqaae9JW8EEIxDKjRQRuQ6oUdXBoqa3AI8NcYkZqroM+DzwgIiErPihqg+p6jJVXVZYGJn15LFITnoKU3LSOZA6x32xRyLIWlUBKVlQGDq77bvVzfzgD7v5SPlk7uwtBhOK4vkw9zrY8H+gvd/vgUDAiat++hE3YrnpF058lVnAZecXcufl5/HYxqP8cYcTlTW1d/HyO9Vcv3AaqclR+PguvIWs7gY+yHZe3x9e3GQ0tNU7FXXWpMiOgnq1ECcaw1Olr9tbw+m2rtEr0d8PpOfAir91yuiTXg3usyk+mlwqjaAUHym+JFYumsYre2r6rpYL4p0TTRTnpFGQFcEpzBgkmj8ZLgVWisingHQgR0R+o6q3ishk4GLghsFOVtVK7+8hEVmPK3QVQkVl9FJW5KeiYRbLW1+CpirIHeOXT+UWmLaozyqi4HrDviQhPSWJ//nZhSQNV0fhstXuH/jHC9w/bW6pmxrY9zwcftUVrl/5H249fRDfuWoOGw7V8w9PbOXe53ZT3eQyiE7yR+kft+wTaEYBn+MN1u69PnLTLv3obHQOL684smVVgtN+h7P896mKSgqz07h0dozXO1hxJ7z+Y/jFx6Gr3TmN9tNw3Y+heGAiyBsXl/LLN47w3M4qvrBiYOrxPSeaE370AFEcQajq3apaqqozcctl16rqrd7hzwDPqWrInzkiki8iad72ZJyzCV2qyjhLWZGfdc3eF85Y4xDdnXByZ58Adf+60t0BpbNbefNA3fDXO/UuSBJ0nAbUKW1f+Ce3jPX6f3ciqxDz8Sm+JK5fOJWObj3rHAB+uv5gdGqHJ6ciC27iY7KZjXuPEBgs9ccYCTSdpEeF/MmRnmLqVVMPv5KpvrWT9ftqWLVo2qC5smKGA392FQ+72gB1zkF8Lv18CBaU5FBe5OfpioGfoc7uAAdqzEFAFB3EMNxMv+klEVkmIr3B7AtwRYq2A+uA+1TVHMQwlBf72dpZiiYlj10PUb0Lejr7xB9C1ZXu7Alw/4v7hr/eK/cOVNiCm/dfejsha1x6PPz6kQH72rrCvO9oWHgzqdrJivbX2FU1/Hr50eBrdSpqX3JkB/FTctNJkvDU1M/tqKKrR2N7eqmXV+51mptgtGdQBbeIcMOSEja/18B7dX2XNB+sbaGrR5k7JbFXMME4OQhVXa+q1wU9v0JVX+jXZrOq3uFtv6mqF6rqQu/vL8bDzlinrNBPB6m05M0de6C6aqCCekx1pQdT0oahsh732uElS+nJn82Nvtejlrwvrb2W076CiF83xZfElJz0sNTUT1VUMndKdnz8Uh6ipvRgrFpUgojLYBtMb4A6kZP09RLj40ojmPJi94vneMZct+RvBFW3BlBZAZmTXNZMj8HqR4dVV3oMCtsx3Xc0iOBbfAuXJO1h9+6dUbmFv6uO1tTIqaiDKcnPcKVHh+BgbQvbjzVyUzyMHmBUn69peRl8aPYknt5a2Uc5v+dEM6nJScwaawqXOMAcRBxRkJVKQVYquyhzgeD6McT0Kyvc6CFo6mf11XNI8fWdCgq7hvcYFLYTUjv8Qrd2f07Nn6htDlHMfozk9tTTkR6dVXel+ZnDTjE9XVFJksCnF0U2BjJhjPLzdcPiUo7Wn2HLe+fqke850cSc4uzYj8tEAHsF4oyyIj9vtnurMkYbh+hohtq9A/QPqxaXsHh6HknCyGt4j0FhOyG1w/Nn0Dr1Em7wvc5fIiya6+7qpEBPE8gaey3qUJTkZXCyqZ3untAjyEBAeXprJR8uL6QoJ04KQ47y83XNgilkpPh4KmiayaXYsPgDRHeZqzEBlBf5eX57PpqShVRWuJKaI+XEdkBDKqhPNLXziXnFPHjbspFfd5R1kmFiaodnLvs8s098gzXb/wLLbh3+hDBpqK2iUBTJmTJ841FQkp9BT0Cpbu4ImVtp45F6KhvbuOuaKI7AJoJRfL78aclcPb+YP+6o4p7r59HU3sWplk7mTrH4A9gIIu4oK/LT0B6gq/ii0Y8gzqb47juCqGps41h9Gytmxfia+TCR+avoklRKjz5L1yC/xkdDY41TUafmRWd6Z7i6EGsqjpOV6uOqedFxULHGjUtKaWrvZu3eGkux0Q9zEHFGeZEbGtfmzHc6hu7OkV+kcosLTmf1DaJu9ArpXDwr8qtv3pek53Kq5ONcwxtsOVgdscu21rnpjMyCKDmIs2K5gVqI9q4ent95kk9eOJWM1NBp1BONS8smU5SdxpqKSvaesBVMwZiDiDPKipww6EDK+dDTATW7R36RqoqQ+Zc2HK4nOz05oX5d5X3wi+RLC8c3/T5i1+xsqAIgpzA6K4iGGkG89E41LR3dY6/CF0f4koRVi0tYv6+GNw7WMS03ndzMcFLNxT/mIOKM4pw0stOS2dw5yhKkraeg8WjI+MOGw3Usn1mAb7i0GnFExtxP0EIG1777rwTuyeXk98vY9OwQSQfDoKfJaT8KpkQ2zUYv6Sk+JvvTQqb9XlNxnGm56VxyXmJME4bLjUtK6A4or75bS9Xpdi69b210lPoxhjmIOENEKCv2s7nR73QMI3UQg8Qfaps7OFTbmjjTSx6b/vgL0rWDDOkkSWAKtSzY8i9jchJJrdU0kE1aWpR0HISuC1HT3M5r+0+xanHJ8LmzEoy9J5r7VECrbGzj7jU7E95JmIOIQ8oK/eyvbXWjgJHmZKrc4nImTV3UZ/emIy7+sCLBHMT0ivtJlr4B6gzpZHrFEAVqhiG1rYbGpOi+jqUh6kI8u62KnoBGvo5GHHD/i/sGVEBr6+qJXjqXGMEcRBxSXuznVEsH7YULnZ6hoyX8k6sqYPIcV4gliA2H6shM9bGgJDfC1r6/KdLQNUaKdPSpwLM6T9GSEt0pnpL8DI43tvVJNvj01kouKs2lrMjW+Pdn3NO5xAjmIOKQ3kD1scwLXIK8E9vDO1HVjSBCxh/qWTojP+EKuNdIaLVzjYw+TUZOdz3t6dFJs9FLaX4Gnd0BTrU6Ffjek03srmriBgtOh2Tc07nECIn1354g9C513alejaVw9RCNR+FMHZQs7rv7TCf7qpu5eGZiTS8BHFuymjbtW3uiTVM5tmT1qK6ngQAF2kB3ZoSr4fWj/0qmpysqSU4Srl8YJ6k1IsyEpHOJAcxBxCEleRmkpySx+3Sq0zOEG4fodST9RhCbjjSgmkD6hyCWr7yTXUt/yEkKUYVuTWLXkntZvvLOUV2vsa6aVOlBsqMrUuvVQlQ2ttETUJ7ZVskVcwqZ7E+L6n1jlQlJ5xIDWKqNOCQpSZhd6Gd/TYvTM4Q7gqiqAF8qFM3vs3vj4TpSk5NYOD0vCta+/1m+8k5YeSebfv+fLN96N/4ps0Z9rcaao+QDKbnRqVTXS/AI4s2Dp6hu6uB718VJ5tYoMRHpXN7v2AgiTikv8nOwpsWNBhqPOn3DcFRWwJSLILnvlMqGw/Usmp5HekpiK2/nXfl5zmgazRt+M+prtJxyyyYzCqL7RZSdnkJOejLHG9pYU1FJdnoyH7sgutNaRvxhDiJOKS/OprKxjTNFC92O4fQQgR6o2jZA/9DS0c2uytNckoDTS/3Jys5jd97lzK1/hfa21uFPCEG7p6LOjpKKOpjS/EzerW7mhV0nue6iqQnv4I2RE3UHISI+EdkqIs95zx8RkcMiss17LBrkvC+JyH7v8aVo2xlvzC50K5kO+sqcrmG4OETtPuhqHRB/2HyknoDCxQmSoG840pfeSg5n2L3uiVGd3336BAAFxdFRUffyzNZKDtW2sOFwPW1dPRRmW+zBGDnjMYL4JrCn377VqrrIe2zrf4KIFAD3ACuAi4F7RCQ/+qbGD+XFzkG826BO1zBcHKLXgfTLwbTxcD3JScKSGYkZf+jPvA9dSw0F+HY+PqrzpaWaVk0nKzt6r+czWyu5e81O2rvPCfx+9uqhhFcFGyMnqg5CREqBa4Gfj/DUq4GXVbVeVRuAl4FrIm1fPDOjIJMUn7hAdclSN8Wk/bWiQVRugbQcmFTWZ/eGw/VcWJpLZqqtZwDwJSdzcOqnWHBmI3XVg9c7HoyUtlrqo6yivv/FfbR19fTZ19YVSHhVsDFyoj2CeAC4C+ifTP+/i8gOEfmxiIQa+5YAx4KeH/f2DUBEvioim0Vkc21taNVrIpLsczV1D9S0OF3DGS8J32BUVsC0RZB07iPR1tnDjuONCbm8dSimfOR2kiXA/rW/GvG5GR2naI6yitpUwUakiJqDEJHrgBpV7T+3cTcwF1gOFADfHct9VPUhVV2mqssKC6NT4zdWKS/K5kBN87m4wmBxiK52qN41IP6w9VgDXT3KJRZ/6MOsecs54JvNpANrRnxuTncdbWnRVVGbKtiIFNEcQVwKrBSRI8DjwJUi8htVPaGODuCXuBhDfyqB4CheqbfPGAGzi/wcrT9De8Fcp28YLA5RvQsC3QPiDxsO1SMCS2da+Kc/p2bfQHnPAd7bM7KqfQWBeroyorvc1FTBRqSImoNQ1btVtVRVZwI3A2tV9VYRmQogIgKsAnaFOP1F4CoRyfeC01d5+4wRUF7kJ6BwuKHL6Rsqt4ZuOIiCeuPheuZNzSEn3Yqn9Kfsytvp1iSqXnsk7HNamhrIlA7wF0fPMEwVbESOiYg8PioihYAA24CvAYjIMuBrqnqHqtaLyH8DNnnn3Kuq9RNga0zTm7Rvf00LF5Qsga2POr1DUr/18JUV7ksr51yeno7uHiqONvCFFTPG0+SYYfKU6WzPXMZ5VX8k0PMASb7hNQYN1UfxA74oq6jBVMFGZBgXoZyqrlfV67ztK1X1QlVdoKq3qmqLt3+zqt4RdM7DqlrmPX45HnbGG7MmZ5EkeIHqpU7ncOrdgQ17M7jKuZIpO4+fpqM7YAHqIehe8DmKqeOdt/4YVvumWjdLmp5vX9xGbGBK6jgmPcXHjElZLlDdG1/oH4doPw11+wfGHw67AZs5iMGZ/9GbadYM2jY9Glb7tgbnILILzUEYsYE5iDhndqGf/dUtTt+QljMw5UaVF5coGeggzi/2U5DVNy+TcY70TD97Cq5kfuM6zrScHrZ9d6NTUecXRVdFbRiRwhxEnFNe7OdIXStditM59F/q2uswpp2rAdHdE2DLkXpW2PLWYfFffBuZ0sE7ax8bvnHzSTo0hZx8W45txAbmIOKcskI/XT3Ke3VnXJzh5C7o7jjXoHILFJwHmeemknZXNdHa2WPTS2Ew9+KrOEEhqe8Mn5sp+UwN9ZKHJNm/nREb2Cc1zunNyXSgtzZEoMs5iV6qtobMvwSwwhzEsCT5fBwpvZ75bRXUVh0Zsm16Ry1NyTYqM2IHcxBxTm9W1z6K6t5AdfNJaKocoH/YcLiOWZOzKMpJH09TY5bSy2/HJ8rBtUMvtvN31XEmyipqw4gk5iDinKy0ZEryMlzSvpxpTu/QG4fojT8EBagDAWXj4fqErD89WqaXL2Rf8hyKDz0zZLv8QD2dUVZRG0YkMQeRAJQV+d0Uk4iX2dUbQVRuAfE5lbXHvupmmtq7WXGeOYiR0Fh+E7MCRzi48+2Qx9vbWsmllUCWOQgjdjAHkQCUew6iJ6Au3nBqv9M/VFVA0TxIzTzbdsOhOsD0DyNlzse+RKf6qH39kZDH673U4L6c6KuoDSNSmINIAMqK/HR0B6hsaPOmk9QFpysrBugfNh6ppyQvg9L8zNAXM0KSN3kKu7Muobz6T3R3dQ443lTrsten5ZuDMGIHcxAJwNmVTLXN5/QOu56C9sY+DkLVxR9s9dLo0IW3MIlG3nn92QHH2uqdijprkqmojdjBHEQCUFaYDeAU1ZkFTvew43fuYNAKpoO1rZxq6bTppVEy//KbOE0WnRW/HXCs01NR5xWaitqIHcxBJAC5mSkUZqe5QDVA5mTo9qqLPXYz7HAirw2HXfxhxXm2Vn80pKVnsnfSJ5jf9BrNp/smHw40naRHhfwiG0EYsYM5iAShvMjvlrrueOJc/iWA08fhD9+AHU+w8XA9hdlpzJxk8YfRkvvBL5IhnexZ+5s++32t1TRILr5kq+1txA7mIBKE3qWu+sq9Tk0dTFcb+sq9bDjk4g8SlPbbGBlzlnyUYzKNrD2/67M/rb2WRp+NzIzYwhxEglBe5Kelo9uNGEJx+jgnm9otQD1GJCmJ4x9YyfzOHZx4b9/Z/f6uOlpTzUEYsYU5iARhtlddriMz9DLLMxlTALjYMriOmRlXfBmA99b96uy+3J56OtIti6sRW0TdQYiIT0S2ishz3vNHRWSfiOwSkYdFJGTBYxHpEZFt3mPgukFjRJQXuZVMb5/3d5CS0fdgSgbPFNxBfmYK5Z4jMUbPtFlzeSdlAVOP/h4NBOju6qRAT9OTFd1a1IYRacZjBPFNYE/Q80eBucCFQAZwR6iTgDZVXeQ9VkbZxrhnsj+VvMwUXvJdBtf/BHKnA+L+Xv8THmxYyvKZBSQlWfwhErTM/QwzAsfZv+01GmqrSBIlKXvKRJtlGCMiqg5CREqBa4Gf9+5T1efVA9gIlEbTBsMhIpQV+jlQ3QIXfRa+vQu+3wjf3sWJGddztP6MLW+NIHOuvI0OTaHhzV/RWONU1Cl50ybYKsMYGdEeQTwA3AUE+h/wppZuA14Y5Nx0EdksIm+LyKrBbiAiX/Xaba6trY2I0fFKebGfA7UtA/Zb/YfIk5s/mV3Zl3L+qZdprj4CQOYkcxBGbBE1ByEi1wE1qrplkCb/Cbyqqq8NcnyGqi4DPg88ICKzQzVS1YdUdZmqLisstCDgUMwu9FPf2kldS0ef/RsO15OdlswFU3MmyLL4JHnJLeTTRNI2p4nILbTBshFbRHMEcSmwUkSOAI8DV4rIbwBE5B6gEPiHwU5W1Urv7yFgPbB4sLZGeJQXu0D1WUW1x4ZDdSybmY/P4g8RZd6Hb6CeHBa1uRTgBcWWZsOILaLmIFT1blUtVdWZwM3AWlW9VUTuAK4GblHVAVNPACKSLyJp3vZknLN5J1q2Jgq9K5T2BzmIUy0dHKxtteWtUSAlNY1jaecDoAoN913EpmcfnGCrDCN8JkIH8VOgGHjLW8L6PQARWSYivcHsC4DNIrIdWAfcp6rmIMbI1Nx0slJ9fUYQZ+MPViAo4mx69kHmtG8HXK2mKdSyYMu/mJMwYoZxSQyjqutx00Soash7qupmvCWvqvombhmsEUFE5Fx1OY+Nh+vJSPFxYUnuBFoWn0yvuJ906ZvWJEM6mV5xP6y8c4KsMozwMSV1glFWlM3+muazzzccrmfpjHxSfPZRiDRFGnpVXZGeGmdLDGN02LdCglFW5Ke6qYOm9i5On+li78kmq/8QJWok9Kq6Gpk8zpYYxugwB5Fg9AaqD9S0sOlIPapWfzpaHFuymjZN7bOvTVM5tmT1BFlkGCPDktMnGGW9DqK6hf01zaT6klg0PW+CrYpPlq+8k024WESRnqJGJnNs6WqWW/zBiBHMQSQY0wsySU1O4kBtCxsP17Noeh7pKb6JNituWb7yzrMB6SnewzBiBZtiSjB8ScLsQj/bjjWyq6rJlrcahjEo5iASkLIiPxsP19MTUIs/GIYxKOYgEpDunnMC9rue3MEzWysn0BrDMN6vmINIMJ7ZWskre6rPPj9xup271+w0J2EYxgDMQSQY97+4j84e7bOvrauH+1/cN8gZhmEkKuYgEoyqxrYR7TcMI3ExB5FgTMvLGNF+wzASF3MQCcbqq+eQ0U/3kJHiY/XVcybIIsMw3q+YUC7BWLW4BHCxiKrGNqblZbD66jln9xuGYfRiDiIBWbW4xByCYRjDYlNMhmEYRkjMQRiGYRghMQdhGIZhhMQchGEYhhEScxCGYRhGSERVh28VI4hILfDeKE+fDCRasWDrc/yTaP0F6/NImaGqIevjxpWDGAsisllVl020HeOJ9Tn+SbT+gvU5ktgUk2EYhhEScxCGYRhGSMxBnOOhiTZgArA+xz+J1l+wPkcMi0EYhmEYIbERhGEYhhEScxCGYRhGSBLKQYjINSKyT0QOiMg/hTh+u4jUisg273HHRNgZSYbrs9fmsyLyjojsFpHfjreNkSaM9/nHQe/xuyLSOBF2RpIw+vwBEVknIltFZIeIfGoi7IwkYfR5hoi84vV3vYiUToSdkUJEHhaRGhHZNchxEZGfeK/HDhFZMuabqmpCPAAfcBA4D0gFtgPz+rW5HfhfE23rOPe5HNgK5HvPiyba7mj3uV/7vwcenmi7x+F9fgj4W297HnBkou0ehz7/DviSt30l8OuJtnuMfb4MWALsGuT4p4A/AQJcAmwY6z0TaQRxMXBAVQ+paifwOPDpCbYp2oTT578B/reqNgCoas042xhpRvo+3wI8Ni6WRY9w+qxAjredC1SNo33RIJw+zwPWetvrQhyPKVT1VaB+iCafBv6vOt4G8kRk6ljumUgOogQ4FvT8uLevPzd5w7MnRWT6+JgWNcLp8/nA+SLyhoi8LSLXjJt10SHc9xlvWiXnAAAGEUlEQVQRmQHM4tyXSKwSTp+/D9wqIseB53Ejp1gmnD5vB270tm8AskVk0jjYNlGE/dkPl0RyEOHwB2Cmql4EvAz8aoLtGQ+ScdNMV+B+Tf9MRPIm1KLx42bgSVXtmWhDxoFbgEdUtRQ3FfFrEYn3///vAJeLyFbgcqASSIT3OmLE+wckmEogeERQ6u07i6rWqWqH9/TnwNJxsi1aDNtn3K+MZ1W1S1UPA+/iHEasEk6fe7mZ2J9egvD6/NfAEwCq+haQjkvwFquE8/9cpao3qupi4J+9fTG/IGEIRvLZD4tEchCbgHIRmSUiqbgvh2eDG/Sbr1sJ7BlH+6LBsH0GnsGNHhCRybgpp0PjaWSECafPiMhcIB94a5ztiwbh9Pko8DEAEbkA5yBqx9XKyBLO//PkoFHS3cDD42zjePMs8EVvNdMlwGlVPTGWCyZHxq73P6raLSJ/B7yIWwHxsKruFpF7gc2q+izwDRFZCXTjgkG3T5jBESDMPr8IXCUi7+CG36tVtW7irB4bYfYZ3BfK4+ot/4hlwuzzP+KmD7+NC1jfHst9D7PPVwD/JiIKvAp8fcIMjgAi8hiuT5O9WNI9QAqAqv4UF1v6FHAAOAN8ecz3jOHPiGEYhhFFEmmKyTAMwxgB5iAMwzCMkJiDMAzDMEJiDsIwDMMIiTkIwzAMIyTmIIz3LSLSEkabb4lIZgTvuUpE5kXwem+O4dwW7+80EXlyiHZ5IvJfRnsfwxgMcxBGrPMtYEQOQkR8QxxehUvyFhFU9UMRuEaVqn5miCZ5gDkII+KYgzDe94jIFV4+/ydFZK+IPOqpRb8BTAPWicg6r+1VIvKWiFSIyO9ExO/tPyIiPxKRCuCvRORvRGSTiGwXkadEJFNEPoRT0N/v1YqYLSKLvCSGO0TkaRHJ9663Xlxdic0iskdElovIGhHZLyI/DLK9JWj7uyKy07vnfSH6OcuzfWe/a8zsrQEgIvNFZKNn3w4RKQfuA2Z7++4XEb+4OggV3rU+HXSdPSLyM3G1P14SkQzvWJmI/NmzrUJEZnv7V3uv0w4R+UFE31jj/c9E5zi3hz0GewAt3t8rgNO43DJJuPQYH/aOHQEme9uTcYrZLO/5d4HvBbW7K+jak4K2fwj8vbf9CPCZoGM7gMu97XuBB7zt9cCPvO1v4tJnTwXScPmtJvXrwyeBN4FM73lBiP4+C3zR2/560Lkz8WoAAP8BfMHbTgUygo97+5OBnKDX5ACuRsBMXJaARd6xJ4Bbve0NwA3edjpuVHYVro6EeK/7c8BlE/25sMf4PRIm1YYR82xU1eMAIrIN92X3er82l+Cmh94QEXBfoMG5lv5f0PYC71d6HuDHpWzog4jkAnmq+hdv169wRWh66U3bsRPYrV7eGxE5hEuaFpyy5OPAL1X1DICqhsrrfylwk7f9a+BHIdq8BfyzuOpoa1R1v9fXPqYD/0NELgMCuJTPxd6xw6q6zdveAswUkWygRFWf9mxr9/pxFc5JbPXa+3GJHF8NYZcRh5iDMGKFjqDtHkJ/dgV4WVVvGeQarUHbjwCrVHW7iNyOl7BwlDYF+tkXGMS+cBgy942q/lZENgDXAs+LyJ0MTK74BaAQWKqqXSJyBDcqCLYZ3OuYMcTtBPg3VX1wBPYbcYTFIIxYpxnI9rbfBi4VkTIAEckSkfMHOS8bOCEiKbgv1AHXU9XTQIOIfMQ7dhvwF0bHy8CXe1dciUhBiDZv4JII0s+ms4jIecAhVf0J8HvgIvq+BuAqxtV4zuGjwIyhDFPVZuC4iKzy7pHm2fki8JWgOE6JiBSF1VsjLjAHYcQ6DwEviMg6Va3FZeB9TER24KZj5g5y3r/i5t3fAPYG7X8cWC0iW71A7ZdwQesdwCJcHGLEqOoLuCmpzd4U2XdCNPsm8HUR2cnglcA+C+zyrrEAV2KyDjettktE7gceBZZ51/liv/4Nxm24bMY7cLGSKar6EvBb4C3vWk/S1xEZcY5lczUMwzBCYiMIwzAMIyTmIAzDMIyQmIMwDMMwQmIOwjAMwwiJOQjDMAwjJOYgDMMwjJCYgzAMwzBC8v8BEc0qjciisX0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k in range(len(transformations)):\n",
    "    pylab.plot(distances, eval_counts[k], '-o', label='VQE + ' + transformations[k].value)\n",
    "pylab.xlabel('Interatomic distance')\n",
    "pylab.ylabel('Evaluations')\n",
    "pylab.title('VQE number of evaluations')\n",
    "pylab.legend(loc='upper left');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
